Leading Digital and AI Change in Healthcare
Healthcare executives lead digital and AI change by tying each initiative to a clear organizational decision. They start where value is clear and risk is manageable, involve clinicians in workflow design, and set governance, accountability, and measures before an initiative scales. The Leadership Program in AI from Wharton Executive Education builds this capability in nontechnical senior executives. Over six months, you learn to assess AI opportunities through a strategic business lens, work through the legal, ethical, and regulatory considerations of AI adoption, and build an AI roadmap for a high-value decision in your own organization.
What Makes AI Change in Healthcare Distinctive
Healthcare brings four considerations to AI change: patient safety, regulated health data, complex clinical workflows, and trust. In the Wharton Leadership Program in AI, the phase on AI inputs and outputs and their business, legal, and ethical responsibilities addresses the data and trust questions head-on, with modules on understanding and mitigating bias, interpreting AI and machine learning output, and navigating legal and regulatory barriers, including data privacy law.
Patient safety comes first. Decisions that touch care carry direct consequences for people, so new tools are introduced with evidence, oversight, and clear accountability.
Data is sensitive and regulated. Health information is subject to privacy and regulatory requirements. Leaders need to understand how data is used, stored, and governed before any AI initiative begins.
Clinical workflows are complex. A tool that saves time in one step can add work in another. Successful adoption fits the way clinicians actually work.
Trust is essential. Clinicians, patients, and regulators all need confidence in how a system reaches its outputs and who remains responsible for decisions.
What Healthcare Leaders Focus On
Healthcare executives who lead digital and AI change should focus on five things, from choosing the first problem to preparing the workforce.
- Choosing the right problems. They start with operational and administrative areas where the value is clear, and the risk is manageable, such as scheduling, documentation support, or supply planning, and build experience before moving closer to clinical decisions.
- Involving clinicians early. Clinicians who help shape a tool become its strongest advocates, and they spot practical issues that are invisible from outside the ward or clinic.
- Building governance before scale. Clear rules on data use, validation, monitoring, and accountability make it possible to expand an initiative with confidence.
- Measuring what matters. They define success in terms of outcomes, safety and staff experience, not only efficiency.
- Preparing the workforce. They plan for how roles and training will change, so that teams can work confidently alongside new tools.
Before approving any AI initiative, a healthcare leader can test it against five questions:
| Question | Why it matters |
| What decision or task does this support, and who remains accountable? | Keeps responsibility clear |
| What data does it rely on, and how is that data governed? | Addresses privacy and regulatory requirements |
| How will we know it is working safely? | Sets up validation and monitoring from the start |
| How does it fit the clinical or operational workflow? | Protects adoption and staff time |
| What changes for the people who use it? | Plans for training and role design |
How Nontechnical and Operations Leaders Use AI Day to Day
Nontechnical and operations leaders start using AI day-to-day by applying it to their own analysis and planning first, then directing it at operational decisions they already own, without writing code. The Wharton Leadership Program in AI requires no technical or coding background and builds that habit through its AI leadership toolkit. Effective Prompt Engineering teaches you to design LLM prompts for daily productivity, analytical tasks, and business-specific applications. Applied GenAI Foundations adds hands-on experimentation with agent-driven workflows, and Enterprise AI in Action covers no-code AI examples and modern experimentation frameworks.
For an operations leader in a hospital or health system, that practice turns into a working sequence:
- Pick a decision you own. Scheduling, documentation support, and supply planning are typical starting points because their value is easy to see.
- Ask a business question of the data. The program’s first phase covers using AI to analyze past performance and turning observational data into decisions; the second covers forecasting and prediction.
- Test before you scale. The module on data-driven experimentation with A/B testing shows how to compare a change against the current process before committing to it.
- Build the capability around it. The phase on data and algorithms in the organization covers building data analytics capabilities and using data-driven insights to optimize operations.
Your role is to define the decision, the data, and the measure of success. Technical teams build and validate the tool; you set the direction and own the outcome.
Moving From a Clinical Role Into Leadership
People in healthcare move from clinical roles into leadership by adding operations, finance, strategy, and data capability to the credibility and care knowledge they already have. For clinician-leaders with at least 10 years of work experience who now guide decisions about digital tools, the Wharton Leadership Program in AI adds the data and AI part of that skill set, along with a required six-week short course on leading and managing high-impact teams.
Clinical experience brings credibility with colleagues, first-hand knowledge of how care is delivered, and practiced judgment under pressure. The move into leadership usually adds three areas of capability:
- Operations and finance. Understanding budgets, capacity, staffing, and how services are funded.
- Strategy and change. Setting direction for a service or organization and leading people through change.
- Data and technology. Interpreting performance data and judging where digital tools and AI can improve care and operations.
Clinicians often build these through roles that combine practice and management: leading a service line, taking on a quality or safety improvement role, joining a clinical informatics or digital committee, or chairing a cross-department project. Each widens scope while keeping clinical credibility at the center.
“The best part of the Wharton Leadership Program in AI was the flow and how the content was built. After completion and by having a capstone project, I felt more capable of having some deep discussions within my organization and among industry peers on the opportunities for AI to leverage sales efficiencies.”
– Wagner Fernando Turri
Sales Director, OEM Asia, Hypertherm, Manufacturing
Choosing a Program to Lead AI Initiatives
A general manager who will lead AI initiatives needs a leadership-level program rather than a technical one. Look for a strategic business lens on AI, faculty-led live learning, a capstone applied to your own organization, coverage of governance and regulation, and a time commitment that fits a full-time role. The Leadership Program in AI from Wharton Executive Education is designed around those needs for nontechnical senior executives, over six months at approximately 4 to 6 hours per week.
| What to look for | How the Leadership Program in AI delivers it |
| A leadership perspective | You evaluate AI, machine learning, and analytics through a strategic business lens, not a technical one. No coding background is required. |
| Faculty-led learning | 15 weeks of core modules combine self-paced online learning with faculty-led live sessions, plus live webinars on the latest generative AI developments. Prasanna Tambe, Professor of Operations, Information and Decisions at The Wharton School, is Academic Director. |
| Application to your organization | A capstone built through six structured checkpoints produces an actionable AI adoption roadmap for your own organization. |
| Governance and regulation | Modules cover bias, interpreting AI output, and legal and regulatory barriers, including data privacy laws. |
| Leading people | A required six-week short course on leading and managing high-impact teams. |
| Peer learning | You learn in a cohort with executives from diverse industries and functions, with an optional two-day recognition ceremony and networking event on the University of Pennsylvania campus in Philadelphia. |
The curriculum moves through four phases: data-driven decisions, machine learning and applications, AI inputs and outputs with their business, legal, and ethical responsibilities, and data and algorithms in the organization. In the capstone, you identify a high-value decision point in your organization, conduct a cost-benefit analysis of applying AI, and determine the data and technological infrastructure your solution needs. You also assess the limitations of AI-enabled decisions, examine regulatory and data privacy requirements, and outline the job transformation and employee training needed for adoption. A healthcare executive can build the whole project around a real decision in their own hospital or health system.
Healthcare leaders already take part. Michael Jones, Administrative Director at Children’s Hospital of Philadelphia, said of the program: “I enjoyed the second part of the program, which focused more on data governance and workforce integration.”
The program requires a minimum of 10 years of work experience. On successful completion, you earn a Wharton Executive Education digital certificate.
Common Questions About Leading AI Change in Healthcare
They connect each initiative to a specific decision, start in operational areas with clear value and manageable risk, involve clinicians early, and put governance, accountability, and measures in place before scaling. The Wharton Leadership Program in AI prepares nontechnical senior executives for this work through modules on AI strategy, legal and regulatory barriers, and workforce impact, and a capstone that produces an AI roadmap for their organization.
Choose an executive leadership program that teaches you to judge AI as a business decision, not one that trains you to build models. The Leadership Program in AI from Wharton Executive Education is designed for nontechnical senior executives with at least 10 years of work experience and requires no coding background.
They begin with their own work, using large language models for daily productivity and analysis, and then apply AI to one operational decision they own. In the Wharton Leadership Program in AI, the Effective Prompt Engineering and Applied genAI Foundations components give you hands-on practice with prompts and agent-driven workflows.
They define the decision, the data, and the measure of success, and let technical teams build and validate the tools. The Wharton Leadership Program in AI teaches this leadership-level role, including how to use data-driven insights to optimize operations and how to test changes with A/B experiments, with no coding required.
A certificate shows recruiters that you completed a structured, faculty-led study; the applied work behind it shows what you can lead. When you complete the Wharton Leadership Program in AI, you receive a verified Wharton Executive Education digital certificate by email in the name you registered with. Add it to your profile and describe your capstone: the AI implementation roadmap you built for a high-value decision in your organization, with its cost-benefit analysis, data requirements, and workforce plan.
Most add operations, finance, strategy, and data skills to their clinical experience through roles that combine practice with management, such as leading a service line or a quality improvement program. For clinician-leaders with at least 10 years of work experience, the Wharton Leadership Program in AI builds the AI and data side of that leadership role.
